Prediction Method, Device, Storage Medium and Program Product for Production Efficiency

Abstract

The present disclosure provides a method, apparatus, storage medium, and program product for predicting production efficiency, including: obtaining production transfer information, determining the water level information of the historical production efficiency of the product before transfer at the time of transfer, and obtaining the first historical data of each product in the production task under the water level information, constructing a first function to be fitted according to the first historical data, and fitting the first function to be fitted to obtain the influence degree parameter of the first process, and determining the production efficiency of the product after transfer according to the influence degree parameter of the first process, wherein the production efficiency of the product after transfer is used to adjust the production task. Through the solution provided by the present disclosure, the accuracy and reliability of predicting production efficiency can be achieved. Furthermore, when adjusting the production task based on the predicted production efficiency, the accuracy and effectiveness of the adjustment can be improved, and the technical effects of saving production resources and meeting the production needs of users can be achieved.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular, to a method, device, storage medium, and program product for predicting production efficiency. Background Art

[0002] With the automation and intelligence of product production, there are higher requirements for predicting the production efficiency of products on manufacturing production lines. For example, when the production line switches from producing one product A to producing another product B, the prediction problem of the production efficiency of product B has attracted much attention. In the scenario of Advanced Planning and Scheduling (APS), if the production efficiency after the production line switches models cannot be accurately determined, it will affect the executability of factory production scheduling.

[0003] In related technologies, usually two methods are adopted to predict the production efficiency after the production line switches models. One method is manual judgment, and the other method is to determine according to the number of repeated processes.

[0004] However, the method of manual judgment relies too much on the ability of experts and is easily affected by subjective factors of people, so the accuracy is relatively low. If determined according to the number of repeated processes, since the processes involved in producing products are usually numerous, and the processes involved in different products are usually different, and the complexity differences between processes may be relatively large, it is very difficult to accurately determine the production similarity between two different products, and thus the accuracy of the determined production efficiency is relatively low.

[0005] The content in the background art part is only the information known to the inventor personally, and does not represent that the above information has entered the public domain before the filing date of the present disclosure, nor does it represent that it can become the prior art of the present disclosure. Summary of the Invention

[0006] The present disclosure provides a method, device, storage medium, and program product for predicting production efficiency to solve at least one of the above technical problems.

[0007] In a first aspect, the present disclosure provides a method for predicting production efficiency, including:

[0008] Obtaining production model change information, where the production model change information is used to indicate switching from producing the product before the production model change in the production task to producing the product after the production model change;

[0009] Determining that the production efficiency of the product before the model change at the time of the model change is at the water level information of the historical production efficiency of the product before the model change, and obtaining first historical data of each product in the production task under the water level information, where the first historical data includes a first process and a first production efficiency;

[0010] Construct a first function to be fitted based on the first historical data, where the first function to be fitted is used to characterize the relationship between the first production efficiency, the preset variables of the first process, and the influence degree parameter of the first process on the first production efficiency, and fit the first function to be fitted to obtain the influence degree parameter of the first process;

[0011] Determine the production efficiency of the product after the model change according to the influence degree parameter of the first process, where the production efficiency of the product after the model change is used to adjust the production task.

[0012] In some embodiments, the determining the production efficiency of the product after the model change according to the influence degree parameter of the first process includes:

[0013] Determine the repeated processes and non-repeated processes between the product before the model change and the product after the model change;

[0014] Obtain the influence degree parameter of the repeated process from the influence degree parameters of the first process;

[0015] Determine the production efficiency of the product after the model change according to the influence degree parameter of the repeated process and the non-repeated process.

[0016] In some embodiments, the determining the production efficiency of the product after the model change according to the influence degree parameter of the repeated process and the non-repeated process includes:

[0017] Determine the production efficiency of the product after the model change according to the influence degree parameter of the repeated process and the historical production efficiency of the non-repeated process.

[0018] In some embodiments, the determining the production efficiency of the product after the model change according to the influence degree parameter of the repeated process and the historical production efficiency of the non-repeated process includes:

[0019] Determine a second production efficiency less than a preset threshold from the historical production efficiencies of the non-repeated processes;

[0020] Construct a second function to be fitted according to the non-repeated process and the second production efficiency, where the second function to be fitted is used to characterize the relationship between the second production efficiency, the preset variables of the non-repeated process, and the influence degree parameter of the non-repeated process on the second production efficiency, and fit the second function to be fitted to obtain the influence degree parameter of the non-repeated process;

[0021] Determine the production efficiency of the product after the model change according to the influence degree of the repeated process and the influence degree parameter of the non-repeated process.

[0022] In some embodiments, determining the production efficiency of the product after the production transfer according to the influence degree parameter of the repeated process and the influence degree parameter of the non-repeated process includes:

[0023] Calculating the sum value of the influence degree parameter of the repeated process and the influence degree parameter of the non-repeated process, and determining the sum value as the production efficiency of the product after the production transfer.

[0024] In some embodiments, the method further includes:

[0025] Obtaining product information of a production task, where the product information includes products and processes, and the products include the product before the production transfer and the product after the production transfer;

[0026] Creating a product list and a process list according to the product information, and determining the corresponding relationship between the product and the process according to the product list and the process list, where the corresponding relationship represents the products under the same process, the corresponding relationship is used to determine the repeated process and the non-repeated process, and the corresponding relationship is also used to construct the first function to be fitted and the second function to be fitted.

[0027] In some embodiments, the production task includes formulating production scheduling information for the product to be produced, and the product to be produced includes the product after the production transfer and the product produced after the product after the production transfer; the method further includes:

[0028] Determining the predicted production scheduling information of the product to be produced according to the production efficiency of the product after the production transfer;

[0029] Matching the predicted production scheduling information with the formulated production scheduling information to obtain a matching result;

[0030] Adjusting the formulated production scheduling information based on the matching result to adjust the production task.

[0031] In a second aspect, the present disclosure provides a prediction device for production efficiency, including:

[0032] An obtaining unit, configured to obtain production transfer information, where the production transfer information is used to indicate switching from producing a product before production transfer in a production task to producing a product after production transfer;

[0033] A determining unit, configured to determine that the production efficiency of the product before the production transfer at the time of production transfer is at the water level information of the historical production efficiency of the product before the production transfer;

[0034] The obtaining unit is further configured to obtain first historical data of each product in the production task under the water level information, where the first historical data includes a first process and a first production efficiency;

[0035] A construction unit, configured to construct a first function to be fitted according to the first historical data, where the first function to be fitted is used to characterize the relationship between the first production efficiency, preset variables of the first process, and the influence degree parameter of the first process on the first production efficiency;

[0036] A fitting unit, configured to fit the first function to be fitted to obtain the influence degree parameter of the first process;

[0037] The determination unit is further configured to determine the production efficiency of the product after the model change according to the influence degree parameter of the first process, where the production efficiency of the product after the model change is used to adjust the production task.

[0038] In some embodiments, the determination unit is specifically configured to determine the repeated processes and non-repeated processes between the product before the model change and the product after the model change; obtain the influence degree parameter of the repeated processes from the influence degree parameters of the first process; and determine the production efficiency of the product after the model change according to the influence degree parameter of the repeated processes and the non-repeated processes.

[0039] In some embodiments, the determination unit is specifically configured to determine the production efficiency of the product after the model change according to the influence degree parameter of the repeated processes and the historical production efficiency of the non-repeated processes.

[0040] In some embodiments, the determination unit is specifically configured to determine, from the historical production efficiency of the non-repeated processes, a second production efficiency that is less than a preset threshold;

[0041] Construct a second function to be fitted according to the non-repeated processes and the second production efficiency, where the second function to be fitted is used to characterize the relationship between the second production efficiency, preset variables of the non-repeated processes, and the influence degree parameter of the non-repeated processes on the second production efficiency, and fit the second function to be fitted to obtain the influence degree parameter of the non-repeated processes;

[0042] Determine the production efficiency of the product after the model change according to the influence degree of the repeated processes and the influence degree parameter of the non-repeated processes.

[0043] In some embodiments, the determination unit is specifically configured to calculate the sum value of the influence degree parameter of the repeated processes and the influence degree parameter of the non-repeated processes, and determine the sum value as the production efficiency of the product after the model change.

[0044] In some embodiments, the obtaining unit is further configured to obtain product information of a production task, where the product information includes products and processes, and the products include the products before transfer of funds and the products after transfer of funds;

[0045] The prediction device further includes:

[0046] A creation unit, configured to create a product list and a process list according to the product information;

[0047] The determining unit is further configured to determine the correspondence between products and processes according to the product list and the process list, where the correspondence characterizes products under the same process, the correspondence is used to determine the repetitive processes and the non-repetitive processes, and the correspondence is further used to construct the first function to be fitted and the second function to be fitted.

[0048] In some embodiments, the production task includes scheduling information for products to be produced, and the products to be produced include the products after transfer of funds and the products produced after the products after transfer of funds; the determining unit is further configured to determine predicted scheduling information for the products to be produced according to the production efficiency of the products after transfer of funds;

[0049] The prediction device further includes:

[0050] A matching unit, configured to match the predicted scheduling information with the scheduled scheduling information to obtain a matching result;

[0051] An adjustment unit, configured to adjust the scheduled scheduling information based on the matching result to adjust the production task.

[0052] In a third aspect, the present disclosure provides a processor-readable storage medium storing a computer program for causing the processor to execute the method described in the first aspect above.

[0053] In a fourth aspect, the present disclosure provides a computer program product including a computer program, where the computer program, when executed by a processor, implements the method described in the first aspect above.

[0054] The prediction method, device, storage medium, and program product for production efficiency provided by the present disclosure include: obtaining production transfer information, where the production transfer information is used to indicate the production switch from the product before production transfer in the production task to the product after production transfer, determining that the production efficiency of the product before transfer at the time of transfer is at the water level information of the historical production efficiency of the product before transfer, and obtaining the first historical data of each product in the production task under the water level information, where the first historical data includes the first process and the first production efficiency, constructing a first function to be fitted according to the first historical data, the first function to be fitted is used to characterize the relationship between the first production efficiency, the preset variables of the first process, and the influence degree parameter of the first process on the first production efficiency, fitting the first function to be fitted to obtain the influence degree parameter of the first process, and determining the production efficiency of the product after transfer according to the influence degree parameter of the first process, where the production efficiency of the product after transfer is used to adjust the production task. In this embodiment, by determining the influence degree parameter of each process on the production efficiency respectively and predicting the production efficiency of the product after transfer based on the influence degree parameter, the disadvantages of low accuracy caused by manual prediction or prediction according to the number of repeated processes in the related art can be avoided, so that the accuracy and reliability of predicting the production efficiency can be realized. Furthermore, when adjusting the production task based on the predicted production efficiency, the accuracy and effectiveness of the adjustment can be improved, and the technical effects of saving production resources and meeting the production needs of users can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.

[0056] Figure 1 It is a schematic flowchart of the prediction method for production efficiency provided by an embodiment of the present disclosure;

[0057] Figure 2 It is a schematic structural diagram of an electronic device when the prediction device for production efficiency provided by an embodiment of the present disclosure is an electronic device;

[0058] Figure 3 It is a schematic module diagram of the prediction device for production efficiency provided by an embodiment of the present disclosure.

[0059] Through the above-mentioned accompanying drawings, the clear embodiments of the present disclosure have been shown, and there will be more detailed descriptions hereinafter. These drawings and text descriptions are not intended to limit the scope of the concept of the present disclosure in any way, but to explain the concept of the present disclosure to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of apparatuses and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0061] It should be understood that the terms "include" and "have" and any variations thereof in the embodiments of the present disclosure are intended to cover but not exclude inclusion. For example, a product or device including a series of components does not necessarily have to be limited to those components clearly listed, but may include other components not clearly listed or inherent to these products or devices.

[0062] The term "and / or" in the embodiments of the present disclosure describes the association relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0063] The term "plurality" in the embodiments of the present disclosure means two or more, and other quantifiers are similar thereto.

[0064] The terms "first", "second", "third", etc. in the present disclosure are used to distinguish similar or like objects or entities, and do not necessarily mean to limit a specific order or sequence, unless otherwise indicated. It should be understood that such terms can be interchanged under appropriate circumstances, for example, they can be implemented in an order other than those given in the illustrations or descriptions of the embodiments of the present disclosure.

[0065] The term "unit / module" used in the present disclosure refers to any known or later developed hardware, software, firmware, artificial intelligence, fuzzy logic, or a combination of hardware or / and software code that can perform functions related to the element.

[0066] Next, the technical solutions in the embodiments of the present disclosure will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.

[0067] Please refer to Figure 1 , Figure 1 is a schematic flowchart of the production efficiency prediction method (hereinafter simply referred to as the prediction method) provided by the embodiments of the present disclosure. AsFigure 1 As shown, the method includes S101 to S104:

[0068] S101: Obtain production transfer information, where the production transfer information is used to indicate switching from producing the product before production transfer in the production task to producing the product after production transfer.

[0069] Exemplarily, the execution subject of the prediction method in this embodiment may be a production efficiency prediction device (hereinafter simply referred to as the prediction device). The prediction device may be a server (such as a local server or a cloud server), may also be an electronic device, may also be a processor, may also be a chip, etc., and this embodiment does not make a limitation.

[0070] If the prediction device is an electronic device, then in some embodiments, the structural diagram of the electronic device may be referred to Figure 2 . Among them, the electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as, a personal digital assistant, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0071] As Figure 2 shown, the electronic device 200 includes a computing unit 201, which can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 202 or the computer program loaded from the storage unit 208 into the random access memory (RAM) 203. In the RAM 203, various programs and data required for the operation of the electronic device 200 can also be stored. The computing unit 201, the ROM 202, and the RAM 203 are connected to each other through a bus 204. The input / output (I / O) interface 205 is also connected to the bus 204.

[0072] A plurality of components in the electronic device 200 are connected to the I / O interface 205, including: an input unit 206, such as a keyboard, a mouse, etc.; an output unit 207, such as various types of displays, speakers, etc.; a storage unit 208, such as a disk, an optical disc, etc.; and a communication unit 209, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 209 allows the electronic device 200 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0073] The computing unit 201 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 201 executes the prediction method and processing described in this disclosure. For example, in some embodiments, the prediction method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 208. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 200 via the ROM 202 and / or the communication unit 209. When the computer program is loaded into the RAM 203 and executed by the computing unit 201, one or more steps of the prediction method of this disclosure can be executed. Alternatively, in other embodiments, the computing unit 201 can be configured to execute the prediction method of this disclosure in any other suitable manner (e.g., by means of firmware).

[0074] It should be noted that the manner in which the prediction device obtains the production transfer information is not limited in this embodiment. For example, the prediction device can include a display device and can output an interaction interface through the display device, and the user can initiate the production transfer information through the interaction interface.

[0075] Among them, the prediction device can display the production tasks through the interaction interface. The production tasks include products and production statuses, such as products that have been produced, products that are being produced, and products that have not been produced. The products before the production transfer are products that have been produced or are being produced, and the products after the transfer are products that have not been produced, and specifically are the products that need to be produced after the products before the transfer are produced.

[0076] S102: Determine that the production efficiency of the products before the transfer at the time of the transfer is at the water level information of the historical production efficiency of the products before the transfer, and obtain the first historical data of each product in the production task under the water level information, where the first historical data includes the first process and the first production efficiency.

[0077] Among them, the water level information can be understood as a percentage. The production efficiency of the products before the transfer at the time of the transfer, relative to the historical production efficiency of the products before the transfer, reaches the percentage of the historical production efficiency. For example, it reaches 80% of the historical production efficiency.

[0078] Since this article involves historical data in different situations, therefore, the "first" and "second" methods are used to distinguish the historical data in different situations, and it cannot be understood as a limitation on the historical data. The descriptions of other "first" and "second" are similar, and will not be explained later.

[0079] Correspondingly, in combination with the above example, if the water level information is 80%, the prediction device can obtain the historical data (i.e., the first historical data) of all products in the production task at 80%. For example, the prediction device includes a memory, and the memory stores data, including historical data. The first historical data includes two dimensions: process (i.e., the first process) and production efficiency (i.e., the first production efficiency). For example, what processes are included for a certain product at 80%, and what the corresponding production efficiency is. That is, the first process is the process for producing all the products.

[0080] S103: Construct a first function to be fitted according to the first historical data. The first function to be fitted is used to characterize the relationship between the first production efficiency, the preset variables of the first process, and the influence degree parameters of the first process on the first production efficiency, and fit the first function to be fitted to obtain the influence degree parameters of the first process.

[0081] Correspondingly, in the case of obtaining the first historical data, the prediction device can construct a function (i.e., the first function to be fitted) according to the first historical data. For example, the first function to be fitted can be represented by Equation 1, Equation 1:

[0082] y = p1*X1 + p2*X2 + …… + pn*Xn

[0083] Where y is the first production efficiency, X1 to Xn are the preset variables corresponding to each first process respectively, and p1 to pn are the influence degree parameters of each process on the corresponding first production efficiency respectively. For example, different processes may have different levels of difficulty, so the influence degrees of different processes on production efficiency may also be different. Therefore, in this embodiment, the influence degree of the process on production efficiency is characterized by the influence degree parameters.

[0084] In this embodiment, fitting can be understood as finding a function that can best describe or predict the patterns or trends in the data. The way of fitting in this embodiment is not limited. For example, linear programming methods can be used for fitting, Lasso regression can also be used for fitting, or linear programming methods and Lasso regression can be combined for fitting. This embodiment focuses on the application of fitting in production efficiency prediction, and specifically on the application of the influence degree of the process on production efficiency, so as to be further applied to the prediction of production efficiency and further applied to the adjustment of production tasks.

[0085] S104: Determine the production efficiency of the product after the model change according to the influence degree parameters of the first process, where the production efficiency of the product after the model change is used to adjust the production task.

[0086] When the influence degree of each process on production efficiency is determined, the prediction device can predict the production efficiency of the product after the product model change based on the influence degree parameters corresponding to each process, so as to obtain the production efficiency of the product after the product model change. On this basis, the prediction device can adjust the production task based on the production efficiency of the product after the product model change, such as adjusting the start production time of a certain product in the production task, etc.

[0087] In some embodiments, based on the above examples, the prediction device can output an interactive interface. Correspondingly, when the prediction device determines the production efficiency of the product after the product model change, it can output the production efficiency of the product after the product model change through the interactive interface.

[0088] Combined with the above analysis of S101 to S104, it can be seen that in this embodiment, the prediction device can determine the influence degree parameters corresponding to each process on production efficiency, and predict the production efficiency of the product after the product model change based on the influence degree parameters, which can avoid the disadvantage of low accuracy caused by manual prediction or prediction based on the number of repeated processes in the related art. Thus, the accuracy and reliability of predicting production efficiency can be achieved. Furthermore, when adjusting the production task based on the predicted production efficiency, the accuracy and effectiveness of the adjustment can be improved, and the technical effect of saving production resources and meeting the production needs of users can be achieved.

[0089] In some embodiments, S104 may include the following steps 1 to 3:

[0090] Step 1: Determine the repeated processes and non-repeated processes between the product before the product model change and the product after the product model change.

[0091] Combined with the above analysis, it can be seen that each product is completed based on the corresponding processes, and usually there are multiple processes for producing each product.

[0092] Exemplarily, the prediction device can determine the processes for producing the product before the product model change, and can also determine among the processes for producing the product after the product model change, and can compare the processes for producing the product before the product model change and the processes for producing the product after the product model change, so as to determine the processes required for both the product before the product model change and the product after the product model change (i.e., repeated processes), and can determine the processes other than the repeated processes among the processes for producing the product after the product model change as non-repeated processes.

[0093] Step 2: Obtain the influence degree parameters of the repeated processes from the influence degree parameters of the first process.

[0094] Combined with the above analysis, it can be seen that the first process is the process for producing all the products. Therefore, the first process includes repeated processes. So, the prediction device can obtain the influence degree parameters of the repeated processes from the influence degree parameters of the first process.

[0095] Step 3: Determine the production efficiency of the product after the model change based on the influence degree parameter of the repetitive process and the non-repetitive process.

[0096] Exemplarily, the process of producing the product after the model change includes a repetitive process and a non-repetitive process. Therefore, the production efficiency of the product after the model change is mainly affected by the repetitive process and the non-repetitive process, and the influence of the repetitive process on the production efficiency can be characterized by the influence degree parameter of the repetitive process. Thus, in this embodiment, the prediction device can determine the production efficiency of the product after the model change based on the influence degree parameter of the repetitive process. Additionally, since the production of the product after the model change also involves a non-repetitive process, the prediction device also considers the non-repetitive process on the basis of considering the influence degree parameter of the repetitive process, so as to determine the production efficiency of the product after the model change by combining the considerations of the two dimensions, thereby improving the effectiveness and accuracy of the determination of the production efficiency of the product after the model change.

[0097] In some embodiments, Step 3 may include: determining the production efficiency of the product after the model change according to the influence degree parameter of the repetitive process and the historical production efficiency of the non-repetitive process.

[0098] Exemplarily, in combination with the above analysis, the prediction device may include a memory, and data may be stored in the memory. For example, the historical production efficiency of the non-repetitive process may be stored in the memory.

[0099] Correspondingly, the prediction device can obtain the historical production efficiency of the non-repetitive process from the memory, and predict the production efficiency of the product after the model change by combining the influence degree parameter of the repetitive process and the historical production efficiency of the non-repetitive process obtained from the memory.

[0100] Relatively speaking, for the same process, the production efficiency may vary when operated by the same worker or different workers, but the difference is not too large. Therefore, in this embodiment, by combining the historical production efficiency of the non-repetitive process and the influence degree parameter of the repetitive process to determine the production efficiency of the product after the model change, the accuracy and reliability of determining the production efficiency of the product after the model change can be improved.

[0101] In some embodiments, the above "determine the production efficiency of the product after the model change according to the influence degree parameter of the repetitive process and the historical production efficiency of the non-repetitive process" may include the following Sub-step 1 and Sub-step 3:

[0102] Sub-step 1: Determine the second production efficiency less than the preset threshold from the historical production efficiency of the non-repetitive process.

[0103] Exemplarily, the memory of the prediction device may store the production efficiency when producing products based on non-repetitive processes in history (i.e., the historical production efficiency of non-repetitive processes). The prediction device may compare all the historical production efficiencies of non-repetitive processes with a preset threshold to obtain the historical production efficiencies that are less than the preset threshold therefrom.

[0104] Herein, the size of the preset threshold is not limited in this embodiment. For example, the prediction device may determine the preset threshold based on requirements, historical records, experiments, etc. Specifically, the preset threshold may take a relatively small value. For example, the preset threshold may be 20% or the like.

[0105] Sub-step 2: Construct a second function to be fitted according to the non-repetitive process and the second production efficiency. The second function to be fitted is used to characterize the relationship between the second production efficiency, the preset variables of the non-repetitive process, and the influence degree parameters of the non-repetitive process on the second production efficiency, and fit the second function to be fitted to obtain the influence degree parameters of the non-repetitive process.

[0106] It can be understood that, in order to avoid stating in a cumbersome manner, for the content similar to that already described above in this embodiment, this embodiment will not be elaborated again.

[0107] For example, for the understanding of the second fitting function in sub-step 2, reference may be made to the description of the first fitting function in the above example, which will not be elaborated here.

[0108] Similarly, the prediction device can fit the second fitting function to obtain the influence degree parameters of the non-repetitive process.

[0109] In some other embodiments, the prediction device may pre-construct functions for fitting different water level information, thereby obtaining corresponding influence degree parameters and storing the influence degree parameters in the memory. When there is a need, the influence degree parameters corresponding to the corresponding requirements can be obtained from the memory.

[0110] Sub-step 3: Determine the production efficiency of the product after transfer according to the influence degree of the repetitive process and the influence degree parameters of the non-repetitive process.

[0111] Combined with the above analysis, it can be seen that after the production transfer, the products involve both repetitive processes and non-repetitive processes. Correspondingly, when the influence degree parameters corresponding to the repetitive process and the non-repetitive process are obtained respectively, the prediction device can combine the influence degree parameters corresponding to the repetitive process and the non-repetitive process to predict the production efficiency of the products after the transfer. This can make the production efficiency of the products after the transfer be associated with each process of the products after the production transfer respectively, thereby improving the accuracy of determining the production efficiency of the products after the transfer. Moreover, in this embodiment, the influence degree parameter of the non-repetitive process is determined based on the second production efficiency under a relatively small preset threshold, which can consider the possible adaptability problems of the transfer, and thus can further improve the accuracy and reliability of the determined production efficiency of the products after the transfer.

[0112] In some embodiments, sub-step 3 may include: calculating the sum value of the influence degree parameters of the repetitive process and the influence degree parameters of the non-repetitive process, and determining the sum value as the production efficiency of the products after the transfer.

[0113] Exemplarily, the prediction device performs a summation calculation on the influence degree parameters of each repetitive process and the influence degree parameters of each non-repetitive process, and uses the sum value obtained from this summation calculation as the production efficiency of the products after the transfer.

[0114] In some embodiments, in order to facilitate the construction and fitting of the first function to be fitted and the second function to be fitted, and to facilitate determining the relationships between the processes corresponding to each product (such as which processes are the same and which are different, etc.), the prediction method further includes the following Step 1 and Step 2:

[0115] Step 1: Obtain the product information of the production task, where the product information includes products and processes, and the products include products before the transfer and products after the transfer.

[0116] Exemplarily, combined with the above analysis, the production task is used to indicate the production of different products. Correspondingly, the product information includes each product indicated by the production task for production, and various processes for producing each product. The products before the transfer and the products after the transfer are two different styles of products in the production task. Generally speaking, at least some of the processes involved in two different products are different.

[0117] Step 2: Create a product list and a process list according to the product information, and determine the corresponding relationship between the product and the process according to the product list and the process list, where the corresponding relationship represents the products under the same process, the corresponding relationship is used to determine the repetitive process and the non-repetitive process, and the corresponding relationship is also used to construct the first function to be fitted and the second function to be fitted.

[0118] Exemplarily, the prediction device can create a product list as shown in Table 1 according to the products in the product information.

[0119] Table 1

[0120] Product 1 Product 2 …… Product m K1 K2 …… Km

[0121] As can be seen from Table 1, the production tasks include products of m (m is an integer greater than or equal to 2) styles, namely products 1 to product m, and can be represented by K1 to Km.

[0122] The prediction device can create a process list as shown in Table 2 according to the processes in the product information.

[0123] Table 2

[0124] Process 1 Process 2 …… Process n G1 G2 …… Gn

[0125] As can be seen from Table 2, the production of m products involves n (n is an integer greater than or equal to 2) processes, namely processes 1 to process n, and can be represented by G1 to Gn.

[0126] The prediction device can analyze the product list and the process list to obtain the corresponding relationship as shown in Table 3.

[0127] Table 3

[0128] Process Product G1 K1, K2, K3 G2 K2, K4, K5, K6 …… ……

[0129] As can be seen from Table 3, the production of products K1, K2, and K3 involves process G1, and the production of products K2, K4, K5, and K6 involves process G2.

[0130] Combined with the above example, in some embodiments, the corresponding relationship shown in Table 3 can be used to determine the repeated processes and non-repeated processes. For example, the prediction device can determine the repeated processes and non-repeated processes by querying Table 3.

[0131] In some other embodiments, the corresponding relationship shown in Table 3 can also be used to construct a first function to be fitted and a second function to be fitted. Exemplarily, taking the corresponding relationship shown in Table 3 can be used to construct a first function to be fitted as an example:

[0132] The prediction device can obtain the historical data (i.e., the first historical data) of all products in the production tasks at 80%, and construct Table 4 of the products and the first production efficiency at 80%.

[0133] Table 4

[0134] Product Production efficiency K1 0.6 K2 1.2 …… …… Km 0.9

[0135] As can be seen from Table 4, the production efficiency of product K1 at 80% is 0.6, the production efficiency of product K2 at 80% is 1.2, and the production efficiency of Km at 80% is 0.9.

[0136] The prediction device constructs Table 5 based on Table 3 and Table 4 to characterize the production efficiency corresponding to each product under each process.

[0137] Table 5

[0138] G1 G2 G3 G4 G5 …… Gn Production efficiency K1 1 1 1 0 0 …… 0 0.6 K2 0 1 1 1 1 …… 0 1.2 …… …… …… …… …… …… …… …… …… Km 1 0 1 0 0 …… 1 0.9

[0139] Among them, 1 indicates the involved process, and 0 indicates the non-involved process. Combining Table 5, taking the production of product K1 as an example, the processes involved in the production of product K1 include G1, G2, G3, etc., and the production efficiency at the 80% water level information is 0.6. Other information in Table 5 is similar, and will not be listed one by one here.

[0140] The prediction device can assign a variable (i.e., a preset variable) to processes G1 to Gn, and can initialize an influence degree parameter for processes G1 to Gn (this step is not necessary), so as to construct Equation 1 as described in the above example. Correspondingly, the prediction device can fit Equation 1 to obtain the influence degree parameters corresponding to processes G1 to Gn respectively.

[0141] It should be noted that in this example, the prediction device determines the corresponding relationship by the above-mentioned method of constructing the table, and constructs and fits the first fitting function and the second fitting function based on the corresponding relationship, which can improve the efficiency of determining the corresponding influence degree parameters, thereby improving the efficiency of predicting the production efficiency of the product after the production transfer. In addition, through the above table and the corresponding relationship, the prediction device can quickly and accurately determine the repeated processes and non-repeated processes, and can also improve the efficiency of predicting the production efficiency of the product after the production transfer.

[0142] In some embodiments, the production task includes formulating production scheduling information for the product to be produced, and the product to be produced includes the product after the production transfer and the product produced after the product after the production transfer. Correspondingly, the prediction method of the present disclosure further includes: determining the predicted production scheduling information of the product to be produced according to the production efficiency of the product after the production transfer, matching the predicted production scheduling information with the formulated production scheduling information to obtain a matching result, and adjusting the formulated production scheduling information based on the matching result to adjust the production task.

[0143] Exemplarily, the formulated production scheduling information can be understood as the pre-determined production scheduling information. The formulated production scheduling information is used to characterize information such as the start production time, production duration, end production time, etc. of the plan for each product in the production task.

[0144] In the case of the production efficiency of the product after receiving the transfer payment, the prediction device can obtain information such as the production duration and the end production time of the product after the transfer payment under this production efficiency. Accordingly, the prediction device can match the information such as the production duration and the end production time with the relevant information in the formulated production scheduling information, and adjust the relevant information in the formulated production scheduling information based on the matching result.

[0145] For example, if the production duration of the product after the transfer payment in the formulated production scheduling information is XX hours, and based on the production efficiency of the product after the transfer payment obtained by prediction, it can be known that the production efficiency of the product after the transfer payment is relatively fast, which may shorten the production duration of the product after the transfer payment. Then the prediction device can adjust the production duration of the product after the transfer payment in the formulated production scheduling information, so as to quickly enter the production of the product after the product after the transfer payment in the production generation task after producing the product after the transfer payment, thereby reducing time consumption and improving the overall production efficiency.

[0146] In some other embodiments, if the matching result indicates that the production efficiency of the product after the transfer payment obtained by prediction cannot meet the production progress of the product after the transfer payment in the formulated production scheduling information, the prediction device can generate and output a prompt message. So that the management personnel can take corresponding measures according to the prompt message to avoid delaying the execution of the production task.

[0147] According to another aspect of the embodiments of the present disclosure, the present disclosure also provides a prediction device. In some embodiments, the structure of the prediction device can refer to Figure 2 , in some other embodiments, the structure of the prediction device can refer to Figure 3 .

[0148] As Figure 3 shown, the prediction device 300 includes:

[0149] An obtaining unit 301, configured to obtain production transfer information, where the production transfer information is used to indicate the production switch from producing the product before the production transfer in the production task to producing the product after the production transfer.

[0150] A determining unit 302, configured to determine that the production efficiency of the product before the transfer at the time of transfer is at the water level information of the historical production efficiency of the product before the transfer.

[0151] The obtaining unit 301 is further configured to obtain first historical data of each product in the production task under the water level information, where the first historical data includes a first process and a first production efficiency.

[0152] A building unit 303 is configured to build a first function to be fitted according to the first historical data, where the first function to be fitted is used to characterize the relationship between the first production efficiency, preset variables of the first process, and influence degree parameters of the first process on the first production efficiency.

[0153] A fitting unit 304 is configured to fit the first function to be fitted to obtain the influence degree parameters of the first process.

[0154] The determining unit 302 is further configured to determine the production efficiency of the product after the product model change according to the influence degree parameters of the first process, where the production efficiency of the product after the product model change is used to adjust the production task.

[0155] In some embodiments, the determining unit 302 is specifically configured to determine the repeated processes and non-repeated processes between the product before the product model change and the product after the product model change; obtain the influence degree parameters of the repeated processes from the influence degree parameters of the first process; and determine the production efficiency of the product after the product model change according to the influence degree parameters of the repeated processes and the non-repeated processes.

[0156] In some embodiments, the determining unit 302 is specifically configured to determine a second production efficiency less than a preset threshold from the historical production efficiencies of the non-repeated processes;

[0157] build a second function to be fitted according to the non-repeated processes and the second production efficiency, where the second function to be fitted is used to characterize the relationship between the second production efficiency, preset variables of the non-repeated processes, and influence degree parameters of the non-repeated processes on the second production efficiency, and fit the second function to be fitted to obtain the influence degree parameters of the non-repeated processes;

[0158] Determine the production efficiency of the product after the product model change according to the influence degree of the repeated processes and the influence degree parameters of the non-repeated processes.

[0159] In some embodiments, the determining unit 302 is specifically configured to calculate the sum value of the influence degree parameters of the repeated processes and the influence degree parameters of the non-repeated processes, and determine the sum value as the production efficiency of the product after the product model change.

[0160] In some embodiments, the obtaining unit 301 is further configured to obtain product information of the production task, where the product information includes products and processes, and the products include the product before the product model change and the product after the product model change;

[0161] The prediction device further includes:

[0162] A creation unit 305 is configured to create a product list and a process list according to the product information;

[0163] The determination unit 302 is further configured to determine the correspondence between products and processes according to the product list and the process list, where the correspondence characterizes the products under the same process, the correspondence is used to determine the repeated processes and the non-repeated processes, and the correspondence is further used to construct the first function to be fitted and the second function to be fitted.

[0164] In some embodiments, the production task includes formulating scheduling information for the products to be produced, and the products to be produced include the products after the product model change and the products produced after the products after the product model change; the determination unit 302 is further configured to determine the predicted scheduling information for the products to be produced according to the production efficiency of the products after the product model change;

[0165] The prediction device further includes:

[0166] A matching unit 306 is configured to match the predicted scheduling information with the formulated scheduling information to obtain a matching result;

[0167] An adjustment unit 307 is configured to adjust the formulated scheduling information based on the matching result to adjust the production task.

[0168] According to an embodiment of the present disclosure, the present disclosure further provides a processor-readable storage medium storing a computer program for causing the processor to execute the prediction method as described above.

[0169] According to an embodiment of the present disclosure, the present disclosure further provides a computer program product including a computer program stored in a readable storage medium. At least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the electronic device to execute the solution provided in any of the above embodiments.

[0170] The various embodiments of the apparatus and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or a general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0171] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general purpose computer, a special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.

[0172] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0173] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0174] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), and the Internet.

[0175] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship between the client and the server is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services ("Virtual Private Server", or simply "VPS"). The server can also be a server of a distributed system, or a server combined with blockchain.

[0176] Those skilled in the art should understand that the embodiments of the present disclosure can be provided as a method, an apparatus, or a computer program product, etc. Therefore, the present disclosure can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0177] This disclosure is described with reference to the flowcharts and / or block diagrams of methods, apparatuses, and computer program products according to embodiments of the disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or in multiple blocks.

[0178] These processor-executable instructions can also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the processor-readable memory produce a manufacture including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or in multiple blocks.

[0179] These processor-executable instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or in multiple blocks.

[0180] Obviously, those skilled in the art can make various changes and modifications to this disclosure without departing from the spirit and scope of this disclosure. Thus, if these modifications and variations of this disclosure fall within the scope of the claims of this disclosure and their equivalent technologies, this disclosure is also intended to include these changes and modifications.

Claims

1. A method for predicting production efficiency, characterized in that: The method comprises: Obtaining production transfer information, wherein the production transfer information is used to indicate switching from producing a product before the production transfer in the production task to producing a product after the production transfer; Determine the water level information that the production efficiency of the pre-transfer product at the time of transfer is at the historical production efficiency of the pre-transfer product, and obtain the first historical data of each product in the production task under the water level information, wherein the first historical data includes a first process and a first production efficiency, and the water level information refers to the percentage of the production efficiency of the pre-transfer product at the time of transfer reaching the historical production efficiency of the pre-transfer product; constructing a first function to be fitted according to the first historical data, where the first function to be fitted is used to characterize the relationship between the first production efficiency, the preset variables of the first process, and a parameter of the degree of influence of the first process on the first production efficiency, and fitting the first function to be fitted to obtain the parameter of the degree of influence of the first process; The production efficiency of the transferred product is determined according to the influence degree parameter of the first process, wherein the production efficiency of the transferred product is used to adjust the production task.

2. The method according to claim 1, characterized in that Determining the production efficiency of the transferred product according to the influence degree parameter of the first process includes: Determine the repeated processes and non-repeated processes between the product before the transfer and the product after the transfer; Obtaining the influence degree parameter of the repeated process from the influence degree parameter of the first process; The production efficiency of the transferred product is determined according to the influence degree parameter of the repeated process and the non-repetitive process.

3. The method according to claim 2, characterized in that The step of determining the production efficiency of the transferred product according to the influence degree parameter of the repeated process and the non-repeated process includes: The production efficiency of the transferred product is determined according to the impact degree parameter of the repeated process and the historical production efficiency of the non-repetitive process.

4. The method according to claim 3, characterized in that The step of determining the production efficiency of the transferred product according to the influence degree parameter of the repeated process and the historical production efficiency of the non-repeated process includes: Determining a second production efficiency less than a preset threshold from historical production efficiencies of the non-repetitive process; Constructing a second function to be fitted according to the non-repetitive process and the second production efficiency, wherein the second function to be fitted is used to characterize the relationship between the second production efficiency, the preset variables of the non-repetitive process, and the parameter of the degree of influence of the non-repetitive process on the second production efficiency, and fitting the second function to be fitted to obtain the parameter of the degree of influence of the non-repetitive process; The production efficiency of the transferred product is determined according to the parameters of the influence degree of the repeated process and the influence degree of the non-repetitive process.

5. The method according to claim 4, characterized in that The step of determining the production efficiency of the transferred product according to the influence degree of the repeated process and the influence degree parameters of the non-repeated process includes: The sum of the impact degree parameter of the repeated process and the impact degree parameter of the non-repetitive process is calculated, and the sum is determined as the production efficiency of the transferred product.

6. The method according to claim 4, characterized in that The method further comprises: Obtaining product information of the production task, the product information including the product and the process, the product including the product before the transfer and the product after the transfer; A product list and a process list are created according to the product information, and a correspondence between products and processes is determined according to the product list and the process list, wherein the correspondence characterizes products under the same process, the correspondence is used to determine the repetitive process and the non-repetitive process, and the correspondence is also used to construct the first function to be fitted and the second function to be fitted.

7. The method according to claim 1, characterized in that The production task includes production scheduling information for products to be produced, and the products to be produced include the transferred products and products produced after the transferred products; the method also includes: Determine the forecast production schedule information of the product to be produced according to the production efficiency of the product after the transfer; Matching the predicted production scheduling information with the formulated production scheduling information to obtain a matching result; The production scheduling information is adjusted based on the matching result to adjust the production task.

8. A production efficiency prediction device, characterized in that: include: An obtaining unit, used for obtaining production transfer information, wherein the production transfer information is used for indicating switching from producing the pre-production transfer product in the production task to producing the post-production transfer product; A determination unit, used to determine the water level information of the production efficiency of the pre-transfer product at the time of transfer, wherein the water level information refers to the percentage of the production efficiency of the pre-transfer product at the time of transfer reaching the historical production efficiency of the pre-transfer product; The obtaining unit is further used to obtain first historical data of each product in the production task under the water level information, wherein the first historical data includes a first process and a first production efficiency; a construction unit, configured to construct a first function to be fitted according to the first historical data, wherein the first function to be fitted is used to characterize a relationship between the first production efficiency, a preset variable of the first process, and a parameter of an influence degree of the first process on the first production efficiency; A fitting unit, used for fitting the first function to be fitted to obtain an influence degree parameter of the first process; The determination unit is further used to determine the production efficiency of the transferred product according to the influence degree parameter of the first process, wherein the production efficiency of the transferred product is used to adjust the production task.

9. A processor-readable storage medium, characterized in that: The processor-readable storage medium stores a computer program, and the computer program is used to enable the processor to execute the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

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